arXiv:2606.04662v1 Announce Type: new Abstract: Muon improves training efficiency over Adam in large language-model training by about two times, but the local geometric source of this advantage remains unclear. Our work takes a first step toward demystifying Muon's superiority over Adam from a curvature perspective. First, we apply a second-order Taylor approximation to the training landscape and show that Muon achieves a larger one-step loss decrease than Adam at matched validation loss. The two optimizers have comparable first-order gains, but Muon consistently incurs a smaller second-order
The paper provides a timely explanation for Muon's observed performance gains over Adam, which has been an empirical finding requiring theoretical underpinning.
Improved optimizer performance directly translates to faster and more efficient training of large AI models, reducing the cost and time-to-market for advanced AI capabilities.
The understanding of optimization landscapes in deep learning now includes a curvature-based explanation for Muon's superiority, which could guide future optimizer development.
- · AI developers
- · Large Language Model companies
- · Cloud providers with advanced AI offerings
- · Inefficient AI training approaches
- · Companies with limited compute resources
Further acceleration of large language model development and deployment.
Reduced operational costs for AI companies, leading to more competitive AI products and services.
Democratization of training capabilities for increasingly complex AI models as efficiency improves.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG